Data Scientist - Predictive Maintenance

**Role Information:** - **Job Title:** Data Scientist - Predictive Maintenance - **Work Location:** Fully remote position, home office - **Employment Type:** Full-time - **Employment Status**: Exempt, salaried - Visa sponsorship is **not** available for this position. - Must reside in the United States. - We are **not** accepting applicants for remote workers in California, Illinois, and New York at this time. **Compensation:** - $98,837 - $154,546, depending on years of experience # **Role Overview:** Applies data science and machine learning to the analysis of electrical, vibration, and acoustic signals, transforming raw time-series sensor data into actionable diagnostics and predictive insights for rotating industrial equipment. Partners with engineering and domain experts to design and deploy production-grade signal processing and ML solutions for predictive maintenance across industrial applications. Operates effectively in ambiguous problem spaces where signal quality, environmental noise, and domain constraints require both technical rigor and adaptive thinking. **Key Responsibilities:** - Design and develop signal processing pipelines and machine learning models that operate on electrical (current/voltage), vibration, and acoustic time-series sensor data, including symmetrical component analysis, matched filtering, wavelet decomposition, and time-frequency analysis techniques. - Evaluate algorithm performance using both objective metrics and subjective measures, including integration with speech recognition engines where applicable. - Perform exploratory data analysis, feature engineering, and signal feature extraction on raw electrical, vibration, and acoustic data to surface fault patterns and anomalies. - Analyze and interpret signals from electrical asset monitoring systems (motors, generators, pumps) utilizing electrical signature analysis, vibration analysis, and signal processing expertise to support fault isolation and anomaly detection. - Use cross-sensor asset monitoring data (temperature, speed, load) to characterize and validate signal-derived diagnostics. - Apply data-driven signal processing methods to characterize and isolate faults at the subsystem, component, and machine level, identifying root causes from spectral, electrical, and vibration sensor data in rotating industrial equipment. - Contribute to end-to-end ML workflows including data ingestion, model training, inference, and monitoring for drift and degradation in live environments. - Collaborate with engineering, product, and domain SMEs to translate operational challenges into well-scoped data science solutions. - Communicate findings, model performance, and business value clearly through visualizations, written documentation, and presentations to technical and non-technical stakeholders. - Explore and evaluate emerging signal processing and AI techniques, recommending production incorporation where appropriate. **Required Qualifications:** - Bachelor’s degree in Electrical Engineering, Computer Engineering, Physics, Applied Mathematics, Acoustical Engineering, Mechanical Engineering, Aerospace Engineering, or a closely related engineering discipline required. - 5+ years of professional experience in data science, machine learning, or applied signal processing, with demonstrated work on electrical, current/voltage, or industrial sensor signal data. - Direct industry experience in one or more of: Industrial/Rotating Equipment, Power Systems, Electrical Machine Diagnostics, or Condition Monitoring. - Hands-on experience with time-series and signal processing techniques, including spectral analysis, filtering, and feature extraction from raw sensor data. - Proficiency in Python, including scientific computing libraries (NumPy, SciPy, pandas) and ML frameworks (scikit-learn, PyTorch, or TensorFlow). - Familiarity with electrical measurement and analysis workflows (e.g., current/voltage waveform capture, power quality analyzers, or equivalent inst…